Mining phenotypes for gene function prediction

<p>Abstract</p> <p>Background</p> <p>Health and disease of organisms are reflected in their phenotypes. Often, a genetic component to a disease is discovered only after clearly defining its phenotype. In the past years, many technologies to systematically generate phenotypes in a high-throughput manner, such as RNA interference or gene knock-out, have been developed and used to decipher functions for genes. However, there have been relatively few efforts to make use of phenotype data beyond the single genotype-phenotype relationships.</p> <p>Results</p> <p>We present results on a study where we use a large set of phenotype data – in textual form – to predict gene annotation. To this end, we use text clustering to group genes based on their phenotype descriptions. We show that these clusters correlate well with several indicators for biological coherence in gene groups, such as functional annotations from the Gene Ontology (GO) and protein-protein interactions. We exploit these clusters for predicting gene function by carrying over annotations from well-annotated genes to other, less-characterized genes in the same cluster. For a subset of groups selected by applying objective criteria, we can predict GO-term annotations from the biological process sub-ontology with up to 72.6% precision and 16.7% recall, as evaluated by cross-validation. We manually verified some of these clusters and found them to exhibit high biological coherence, e.g. a group containing all available antennal Drosophila odorant receptors despite inconsistent GO-annotations.</p> <p>Conclusion</p> <p>The intrinsic nature of phenotypes to visibly reflect genetic activity underlines their usefulness in inferring new gene functions. Thus, systematically analyzing these data on a large scale offers many possibilities for inferring functional annotation of genes. We show that text clustering can play an important role in this process.</p

Second-Generation Sequencing Supply an Effective Way to Screen RNAi Targets in Large Scale for Potential Application in Pest Insect Control

The key of RNAi approach success for potential insect pest control is mainly dependent on careful target selection and a convenient delivery system. We adopted second-generation sequencing technology to screen RNAi targets. Illumina's RNA-seq and digital gene expression tag profile (DGE-tag) technologies were used to screen optimal RNAi targets from Ostrinia furnalalis. Total 14690 stage specific genes were obtained which can be considered as potential targets, and 47 were confirmed by qRT-PCR. Ten larval stage specific expression genes were selected for RNAi test. When 50 ng/µl dsRNAs of the genes DS10 and DS28 were directly sprayed on the newly hatched larvae which placed on the filter paper, the larval mortalities were around 40∼50%, while the dsRNAs of ten genes were sprayed on the larvae along with artificial diet, the mortalities reached 73% to 100% at 5 d after treatment. The qRT-PCR analysis verified the correlation between larval mortality and the down-regulation of the target gene expression. Topically applied fluorescent dsRNA confirmed that dsRNA did penetrate the body wall and circulate in the body cavity. It seems likely that the combination of DGE-tag with RNA-seq is a rapid, high-throughput, cost less and an easy way to select the candidate target genes for RNAi. More importantly, it demonstrated that dsRNAs are able to penetrate the integument and cause larval developmental stunt and/or death in a lepidopteron insect. This finding largely broadens the target selection for RNAi from just gut-specific genes to the targets in whole insects and may lead to new strategies for designing RNAi-based technology against insect damage

The Drosophila melanogaster host model

The deleterious and sometimes fatal outcomes of bacterial infectious diseases are the net result of the interactions between the pathogen and the host, and the genetically tractable fruit fly, Drosophila melanogaster, has emerged as a valuable tool for modeling the pathogen–host interactions of a wide variety of bacteria. These studies have revealed that there is a remarkable conservation of bacterial pathogenesis and host defence mechanisms between higher host organisms and Drosophila. This review presents an in-depth discussion of the Drosophila immune response, the Drosophila killing model, and the use of the model to examine bacterial–host interactions. The recent introduction of the Drosophila model into the oral microbiology field is discussed, specifically the use of the model to examine Porphyromonas gingivalis–host interactions, and finally the potential uses of this powerful model system to further elucidate oral bacterial-host interactions are addressed